Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations792
Missing cells723
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.6 KiB
Average record size in memory104.2 B

Variable types

Text1
Categorical7
Numeric5

Alerts

Crew_Feedback is highly overall correlated with Quantity_Returned and 1 other fieldsHigh correlation
Flight_Type is highly overall correlated with Origin and 2 other fieldsHigh correlation
Origin is highly overall correlated with Flight_Type and 1 other fieldsHigh correlation
Passenger_Count is highly overall correlated with Flight_Type and 3 other fieldsHigh correlation
Product_ID is highly overall correlated with Product_Name and 1 other fieldsHigh correlation
Product_Name is highly overall correlated with Product_ID and 1 other fieldsHigh correlation
Quantity_Consumed is highly overall correlated with Flight_Type and 4 other fieldsHigh correlation
Quantity_Returned is highly overall correlated with Crew_Feedback and 3 other fieldsHigh correlation
Service_Type is highly overall correlated with Crew_Feedback and 3 other fieldsHigh correlation
Standard_Specification_Qty is highly overall correlated with Passenger_Count and 3 other fieldsHigh correlation
Unit_Cost is highly overall correlated with Product_ID and 1 other fieldsHigh correlation
Crew_Feedback has 723 (91.3%) missing valuesMissing

Reproduction

Analysis started2025-10-25 04:32:49.595562
Analysis finished2025-10-25 04:32:50.665912
Duration1.07 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct144
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:50.820938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3960
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAM109
2nd rowAM109
3rd rowAM109
4th rowAM109
5th rowLX110
ValueCountFrequency (%)
lx1907
 
0.9%
ba1087
 
0.9%
nh2207
 
0.9%
lx1197
 
0.9%
qr1917
 
0.9%
ba1747
 
0.9%
am1887
 
0.9%
am1117
 
0.9%
aa1807
 
0.9%
lx1107
 
0.9%
Other values (134)722
91.2%
2025-10-24T22:32:50.975698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1744
18.8%
A555
14.0%
2447
11.3%
0191
 
4.8%
3181
 
4.6%
4158
 
4.0%
M153
 
3.9%
L146
 
3.7%
B146
 
3.7%
X146
 
3.7%
Other values (9)1093
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1744
18.8%
A555
14.0%
2447
11.3%
0191
 
4.8%
3181
 
4.6%
4158
 
4.0%
M153
 
3.9%
L146
 
3.7%
B146
 
3.7%
X146
 
3.7%
Other values (9)1093
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1744
18.8%
A555
14.0%
2447
11.3%
0191
 
4.8%
3181
 
4.6%
4158
 
4.0%
M153
 
3.9%
L146
 
3.7%
B146
 
3.7%
X146
 
3.7%
Other values (9)1093
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1744
18.8%
A555
14.0%
2447
11.3%
0191
 
4.8%
3181
 
4.6%
4158
 
4.0%
M153
 
3.9%
L146
 
3.7%
B146
 
3.7%
X146
 
3.7%
Other values (9)1093
27.6%

Origin
Categorical

High correlation 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
NRT
136 
ZRH
136 
DOH
135 
JFK
134 
MEX
127 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2376
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOH
2nd rowDOH
3rd rowDOH
4th rowDOH
5th rowDOH

Common Values

ValueCountFrequency (%)
NRT136
17.2%
ZRH136
17.2%
DOH135
17.0%
JFK134
16.9%
MEX127
16.0%
LHR124
15.7%

Length

2025-10-24T22:32:51.006897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.027484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
nrt136
17.2%
zrh136
17.2%
doh135
17.0%
jfk134
16.9%
mex127
16.0%
lhr124
15.7%

Most occurring characters

ValueCountFrequency (%)
R396
16.7%
H395
16.6%
N136
 
5.7%
T136
 
5.7%
Z136
 
5.7%
D135
 
5.7%
O135
 
5.7%
J134
 
5.6%
F134
 
5.6%
K134
 
5.6%
Other values (4)505
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R396
16.7%
H395
16.6%
N136
 
5.7%
T136
 
5.7%
Z136
 
5.7%
D135
 
5.7%
O135
 
5.7%
J134
 
5.6%
F134
 
5.6%
K134
 
5.6%
Other values (4)505
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R396
16.7%
H395
16.6%
N136
 
5.7%
T136
 
5.7%
Z136
 
5.7%
D135
 
5.7%
O135
 
5.7%
J134
 
5.6%
F134
 
5.6%
K134
 
5.6%
Other values (4)505
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R396
16.7%
H395
16.6%
N136
 
5.7%
T136
 
5.7%
Z136
 
5.7%
D135
 
5.7%
O135
 
5.7%
J134
 
5.6%
F134
 
5.6%
K134
 
5.6%
Other values (4)505
21.3%

Date
Categorical

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2025-10-05
75 
2025-10-03
71 
2025-09-26
69 
2025-09-27
69 
2025-09-29
67 
Other values (7)
441 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7920
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-09-26
2nd row2025-09-26
3rd row2025-09-26
4th row2025-09-26
5th row2025-09-26

Common Values

ValueCountFrequency (%)
2025-10-0575
9.5%
2025-10-0371
9.0%
2025-09-2669
8.7%
2025-09-2769
8.7%
2025-09-2967
8.5%
2025-10-0667
8.5%
2025-10-0166
8.3%
2025-10-0264
8.1%
2025-10-0764
8.1%
2025-10-0462
7.8%
Other values (2)118
14.9%

Length

2025-10-24T22:32:51.055855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2025-10-0575
9.5%
2025-10-0371
9.0%
2025-09-2669
8.7%
2025-09-2769
8.7%
2025-09-2967
8.5%
2025-10-0667
8.5%
2025-10-0166
8.3%
2025-10-0264
8.1%
2025-10-0764
8.1%
2025-10-0462
7.8%
Other values (2)118
14.9%

Most occurring characters

ValueCountFrequency (%)
02113
26.7%
21911
24.1%
-1584
20.0%
5867
10.9%
1535
 
6.8%
9390
 
4.9%
6136
 
1.7%
7133
 
1.7%
3131
 
1.7%
462
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)7920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02113
26.7%
21911
24.1%
-1584
20.0%
5867
10.9%
1535
 
6.8%
9390
 
4.9%
6136
 
1.7%
7133
 
1.7%
3131
 
1.7%
462
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02113
26.7%
21911
24.1%
-1584
20.0%
5867
10.9%
1535
 
6.8%
9390
 
4.9%
6136
 
1.7%
7133
 
1.7%
3131
 
1.7%
462
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02113
26.7%
21911
24.1%
-1584
20.0%
5867
10.9%
1535
 
6.8%
9390
 
4.9%
6136
 
1.7%
7133
 
1.7%
3131
 
1.7%
462
 
0.8%

Flight_Type
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
long-haul
397 
short-haul
260 
medium-haul
135 

Length

Max length11
Median length9
Mean length9.6691919
Min length9

Characters and Unicode

Total characters7658
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium-haul
2nd rowmedium-haul
3rd rowmedium-haul
4th rowmedium-haul
5th rowmedium-haul

Common Values

ValueCountFrequency (%)
long-haul397
50.1%
short-haul260
32.8%
medium-haul135
 
17.0%

Length

2025-10-24T22:32:51.084513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.215659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
long-haul397
50.1%
short-haul260
32.8%
medium-haul135
 
17.0%

Most occurring characters

ValueCountFrequency (%)
l1189
15.5%
h1052
13.7%
u927
12.1%
-792
10.3%
a792
10.3%
o657
8.6%
n397
 
5.2%
g397
 
5.2%
m270
 
3.5%
s260
 
3.4%
Other values (5)925
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l1189
15.5%
h1052
13.7%
u927
12.1%
-792
10.3%
a792
10.3%
o657
8.6%
n397
 
5.2%
g397
 
5.2%
m270
 
3.5%
s260
 
3.4%
Other values (5)925
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l1189
15.5%
h1052
13.7%
u927
12.1%
-792
10.3%
a792
10.3%
o657
8.6%
n397
 
5.2%
g397
 
5.2%
m270
 
3.5%
s260
 
3.4%
Other values (5)925
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l1189
15.5%
h1052
13.7%
u927
12.1%
-792
10.3%
a792
10.3%
o657
8.6%
n397
 
5.2%
g397
 
5.2%
m270
 
3.5%
s260
 
3.4%
Other values (5)925
12.1%

Service_Type
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Pick & Pack
467 
Retail
325 

Length

Max length11
Median length11
Mean length8.9482323
Min length6

Characters and Unicode

Total characters7087
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRetail
2nd rowRetail
3rd rowRetail
4th rowRetail
5th rowPick & Pack

Common Values

ValueCountFrequency (%)
Pick & Pack467
59.0%
Retail325
41.0%

Length

2025-10-24T22:32:51.239672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.255203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pick467
27.1%
467
27.1%
pack467
27.1%
retail325
18.8%

Most occurring characters

ValueCountFrequency (%)
P934
13.2%
c934
13.2%
k934
13.2%
934
13.2%
i792
11.2%
a792
11.2%
&467
6.6%
R325
 
4.6%
e325
 
4.6%
t325
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)7087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P934
13.2%
c934
13.2%
k934
13.2%
934
13.2%
i792
11.2%
a792
11.2%
&467
6.6%
R325
 
4.6%
e325
 
4.6%
t325
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P934
13.2%
c934
13.2%
k934
13.2%
934
13.2%
i792
11.2%
a792
11.2%
&467
6.6%
R325
 
4.6%
e325
 
4.6%
t325
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P934
13.2%
c934
13.2%
k934
13.2%
934
13.2%
i792
11.2%
a792
11.2%
&467
6.6%
R325
 
4.6%
e325
 
4.6%
t325
 
4.6%

Passenger_Count
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.58207
Minimum121
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:51.279029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile137
Q1163
median276
Q3311
95-th percentile332
Maximum374
Range253
Interquartile range (IQR)148

Descriptive statistics

Standard deviation73.109418
Coefficient of variation (CV)0.29292736
Kurtosis-1.3770865
Mean249.58207
Median Absolute Deviation (MAD)42
Skewness-0.44733765
Sum197669
Variance5344.9869
MonotonicityNot monotonic
2025-10-24T22:32:51.312064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32525
 
3.2%
15225
 
3.2%
31124
 
3.0%
31721
 
2.7%
14520
 
2.5%
30418
 
2.3%
29418
 
2.3%
13717
 
2.1%
27217
 
2.1%
27616
 
2.0%
Other values (86)591
74.6%
ValueCountFrequency (%)
1214
 
0.5%
1236
 
0.8%
1315
 
0.6%
1336
 
0.8%
1357
0.9%
13717
2.1%
1388
1.0%
14113
1.6%
1437
0.9%
1444
 
0.5%
ValueCountFrequency (%)
3747
0.9%
3674
 
0.5%
3497
0.9%
3484
 
0.5%
3464
 
0.5%
3405
0.6%
3366
0.8%
33211
1.4%
3295
0.6%
3284
 
0.5%

Product_ID
Categorical

High correlation 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
CRK075
85 
SNK001
83 
JCE200
82 
CHO050
81 
HTB110
79 
Other values (5)
382 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4752
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRD001
2nd rowCRK075
3rd rowDRK023
4th rowDRK024
5th rowBRD001

Common Values

ValueCountFrequency (%)
CRK07585
10.7%
SNK00183
10.5%
JCE20082
10.4%
CHO05081
10.2%
HTB11079
10.0%
COF20079
10.0%
BRD00177
9.7%
DRK02377
9.7%
DRK02476
9.6%
NUT03073
9.2%

Length

2025-10-24T22:32:51.342316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.367779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
crk07585
10.7%
snk00183
10.5%
jce20082
10.4%
cho05081
10.2%
htb11079
10.0%
cof20079
10.0%
brd00177
9.7%
drk02377
9.7%
drk02476
9.6%
nut03073
9.2%

Most occurring characters

ValueCountFrequency (%)
01267
26.7%
C327
 
6.9%
K321
 
6.8%
1318
 
6.7%
R315
 
6.6%
2314
 
6.6%
D230
 
4.8%
5166
 
3.5%
H160
 
3.4%
O160
 
3.4%
Other values (11)1174
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01267
26.7%
C327
 
6.9%
K321
 
6.8%
1318
 
6.7%
R315
 
6.6%
2314
 
6.6%
D230
 
4.8%
5166
 
3.5%
H160
 
3.4%
O160
 
3.4%
Other values (11)1174
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01267
26.7%
C327
 
6.9%
K321
 
6.8%
1318
 
6.7%
R315
 
6.6%
2314
 
6.6%
D230
 
4.8%
5166
 
3.5%
H160
 
3.4%
O160
 
3.4%
Other values (11)1174
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01267
26.7%
C327
 
6.9%
K321
 
6.8%
1318
 
6.7%
R315
 
6.6%
2314
 
6.6%
D230
 
4.8%
5166
 
3.5%
H160
 
3.4%
O160
 
3.4%
Other values (11)1174
24.7%

Product_Name
Categorical

High correlation 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Butter Cookies 75g
85 
Snack Box Economy
83 
Juice 200ml
82 
Chocolate Bar 50g
81 
Herbal Tea Bag
79 
Other values (5)
382 

Length

Max length21
Median length18
Mean length16.40404
Min length11

Characters and Unicode

Total characters12992
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBread Roll Pack
2nd rowButter Cookies 75g
3rd rowSparkling Water 330ml
4th rowStill Water 500ml
5th rowBread Roll Pack

Common Values

ValueCountFrequency (%)
Butter Cookies 75g85
10.7%
Snack Box Economy83
10.5%
Juice 200ml82
10.4%
Chocolate Bar 50g81
10.2%
Herbal Tea Bag79
10.0%
Instant Coffee Stick79
10.0%
Bread Roll Pack77
9.7%
Sparkling Water 330ml77
9.7%
Still Water 500ml76
9.6%
Mixed Nuts 30g73
9.2%

Length

2025-10-24T22:32:51.407170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.438502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
water153
 
6.7%
butter85
 
3.7%
cookies85
 
3.7%
75g85
 
3.7%
snack83
 
3.6%
box83
 
3.6%
economy83
 
3.6%
juice82
 
3.6%
200ml82
 
3.6%
chocolate81
 
3.5%
Other values (18)1392
60.7%

Most occurring characters

ValueCountFrequency (%)
1502
 
11.6%
e952
 
7.3%
a945
 
7.3%
t790
 
6.1%
l778
 
6.0%
o737
 
5.7%
r552
 
4.2%
0547
 
4.2%
c485
 
3.7%
i472
 
3.6%
Other values (30)5232
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1502
 
11.6%
e952
 
7.3%
a945
 
7.3%
t790
 
6.1%
l778
 
6.0%
o737
 
5.7%
r552
 
4.2%
0547
 
4.2%
c485
 
3.7%
i472
 
3.6%
Other values (30)5232
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1502
 
11.6%
e952
 
7.3%
a945
 
7.3%
t790
 
6.1%
l778
 
6.0%
o737
 
5.7%
r552
 
4.2%
0547
 
4.2%
c485
 
3.7%
i472
 
3.6%
Other values (30)5232
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1502
 
11.6%
e952
 
7.3%
a945
 
7.3%
t790
 
6.1%
l778
 
6.0%
o737
 
5.7%
r552
 
4.2%
0547
 
4.2%
c485
 
3.7%
i472
 
3.6%
Other values (30)5232
40.3%

Standard_Specification_Qty
Real number (ℝ)

High correlation 

Distinct223
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.38889
Minimum34
Maximum306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:51.484747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile43.55
Q178
median114
Q3162
95-th percentile250.9
Maximum306
Range272
Interquartile range (IQR)84

Descriptive statistics

Standard deviation60.973977
Coefficient of variation (CV)0.48627894
Kurtosis-0.16674995
Mean125.38889
Median Absolute Deviation (MAD)40
Skewness0.71369942
Sum99308
Variance3717.8258
MonotonicityNot monotonic
2025-10-24T22:32:51.521234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7914
 
1.8%
10611
 
1.4%
14411
 
1.4%
7711
 
1.4%
7210
 
1.3%
15010
 
1.3%
7410
 
1.3%
739
 
1.1%
949
 
1.1%
449
 
1.1%
Other values (213)688
86.9%
ValueCountFrequency (%)
341
 
0.1%
351
 
0.1%
363
 
0.4%
374
0.5%
381
 
0.1%
392
 
0.3%
407
0.9%
419
1.1%
426
0.8%
436
0.8%
ValueCountFrequency (%)
3061
0.1%
3021
0.1%
2981
0.1%
2892
0.3%
2872
0.3%
2842
0.3%
2831
0.1%
2751
0.1%
2722
0.3%
2701
0.1%

Quantity_Returned
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.042929
Minimum2
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:51.554383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q112
median26
Q349
95-th percentile83.45
Maximum122
Range120
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.225871
Coefficient of variation (CV)0.76342719
Kurtosis0.24192181
Mean33.042929
Median Absolute Deviation (MAD)16
Skewness0.95037557
Sum26170
Variance636.34455
MonotonicityNot monotonic
2025-10-24T22:32:51.589999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
727
 
3.4%
922
 
2.8%
422
 
2.8%
820
 
2.5%
620
 
2.5%
1220
 
2.5%
1120
 
2.5%
1019
 
2.4%
1819
 
2.4%
518
 
2.3%
Other values (93)585
73.9%
ValueCountFrequency (%)
23
 
0.4%
314
1.8%
422
2.8%
518
2.3%
620
2.5%
727
3.4%
820
2.5%
922
2.8%
1019
2.4%
1120
2.5%
ValueCountFrequency (%)
1221
 
0.1%
1171
 
0.1%
1111
 
0.1%
1072
0.3%
1061
 
0.1%
1043
0.4%
1031
 
0.1%
1021
 
0.1%
1011
 
0.1%
1002
0.3%

Quantity_Consumed
Real number (ℝ)

High correlation 

Distinct175
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.34596
Minimum28
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:51.624817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q161
median86
Q3114.25
95-th percentile176
Maximum242
Range214
Interquartile range (IQR)53.25

Descriptive statistics

Standard deviation42.070034
Coefficient of variation (CV)0.45556984
Kurtosis0.5167035
Mean92.34596
Median Absolute Deviation (MAD)26
Skewness0.88211371
Sum73138
Variance1769.8877
MonotonicityNot monotonic
2025-10-24T22:32:51.661283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6015
 
1.9%
9213
 
1.6%
9913
 
1.6%
11012
 
1.5%
6612
 
1.5%
6412
 
1.5%
8511
 
1.4%
9011
 
1.4%
5311
 
1.4%
4410
 
1.3%
Other values (165)672
84.8%
ValueCountFrequency (%)
282
 
0.3%
291
 
0.1%
302
 
0.3%
317
0.9%
323
 
0.4%
331
 
0.1%
346
0.8%
357
0.9%
368
1.0%
379
1.1%
ValueCountFrequency (%)
2421
0.1%
2411
0.1%
2261
0.1%
2231
0.1%
2191
0.1%
2181
0.1%
2162
0.3%
2151
0.1%
2131
0.1%
2121
0.1%

Unit_Cost
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63896465
Minimum0.06
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-10-24T22:32:51.687199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.06
Q10.35
median0.55
Q30.75
95-th percentile2.1
Maximum2.1
Range2.04
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.55402281
Coefficient of variation (CV)0.86706333
Kurtosis2.3364454
Mean0.63896465
Median Absolute Deviation (MAD)0.2
Skewness1.6959183
Sum506.06
Variance0.30694128
MonotonicityNot monotonic
2025-10-24T22:32:51.711789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.7585
10.7%
2.183
10.5%
0.5582
10.4%
0.881
10.2%
0.0679
10.0%
0.0879
10.0%
0.3577
9.7%
0.4577
9.7%
0.576
9.6%
0.6573
9.2%
ValueCountFrequency (%)
0.0679
10.0%
0.0879
10.0%
0.3577
9.7%
0.4577
9.7%
0.576
9.6%
0.5582
10.4%
0.6573
9.2%
0.7585
10.7%
0.881
10.2%
2.183
10.5%
ValueCountFrequency (%)
2.183
10.5%
0.881
10.2%
0.7585
10.7%
0.6573
9.2%
0.5582
10.4%
0.576
9.6%
0.4577
9.7%
0.3577
9.7%
0.0879
10.0%
0.0679
10.0%

Crew_Feedback
Categorical

High correlation  Missing 

Distinct3
Distinct (%)4.3%
Missing723
Missing (%)91.3%
Memory size6.3 KiB
drawer incomplete
43 
low demand
18 
ran out early

Length

Max length17
Median length17
Mean length14.710145
Min length10

Characters and Unicode

Total characters1015
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdrawer incomplete
2nd rowdrawer incomplete
3rd rowdrawer incomplete
4th rowran out early
5th rowdrawer incomplete

Common Values

ValueCountFrequency (%)
drawer incomplete43
 
5.4%
low demand18
 
2.3%
ran out early8
 
1.0%
(Missing)723
91.3%

Length

2025-10-24T22:32:51.739870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T22:32:51.758208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
drawer43
29.5%
incomplete43
29.5%
low18
12.3%
demand18
12.3%
ran8
 
5.5%
out8
 
5.5%
early8
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e155
15.3%
r102
10.0%
d79
7.8%
a77
 
7.6%
77
 
7.6%
n69
 
6.8%
o69
 
6.8%
l69
 
6.8%
w61
 
6.0%
m61
 
6.0%
Other values (6)196
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e155
15.3%
r102
10.0%
d79
7.8%
a77
 
7.6%
77
 
7.6%
n69
 
6.8%
o69
 
6.8%
l69
 
6.8%
w61
 
6.0%
m61
 
6.0%
Other values (6)196
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e155
15.3%
r102
10.0%
d79
7.8%
a77
 
7.6%
77
 
7.6%
n69
 
6.8%
o69
 
6.8%
l69
 
6.8%
w61
 
6.0%
m61
 
6.0%
Other values (6)196
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e155
15.3%
r102
10.0%
d79
7.8%
a77
 
7.6%
77
 
7.6%
n69
 
6.8%
o69
 
6.8%
l69
 
6.8%
w61
 
6.0%
m61
 
6.0%
Other values (6)196
19.3%

Interactions

2025-10-24T22:32:50.412654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.820796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.973285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.123555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.274168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.446363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.853286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.001512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.151719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.300655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.483652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.884749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.030602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.182377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.328303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.516357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.914677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.061806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.211976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.356416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.549000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:49.942770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.092281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.240658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T22:32:50.382321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-24T22:32:51.779549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Crew_FeedbackDateFlight_TypeOriginPassenger_CountProduct_IDProduct_NameQuantity_ConsumedQuantity_ReturnedService_TypeStandard_Specification_QtyUnit_Cost
Crew_Feedback1.0000.1050.0000.0000.0000.0000.0000.0220.5200.5820.3840.000
Date0.1051.0000.0000.0000.2580.0000.0000.0580.0710.2370.0930.000
Flight_Type0.0000.0001.0000.9980.8650.0000.0000.5220.2820.0940.4830.000
Origin0.0000.0000.9981.0000.5790.0000.0000.3340.1920.1130.3070.000
Passenger_Count0.0000.2580.8650.5791.0000.0000.0000.6980.3820.2150.6410.006
Product_ID0.0000.0000.0000.0000.0001.0001.0000.1500.1000.0000.2040.997
Product_Name0.0000.0000.0000.0000.0001.0001.0000.1500.1000.0000.2040.997
Quantity_Consumed0.0220.0580.5220.3340.6980.1500.1501.0000.6620.5040.950-0.055
Quantity_Returned0.5200.0710.2820.1920.3820.1000.1000.6621.0000.8230.8540.043
Service_Type0.5820.2370.0940.1130.2150.0000.0000.5040.8231.0000.6800.000
Standard_Specification_Qty0.3840.0930.4830.3070.6410.2040.2040.9500.8540.6801.000-0.021
Unit_Cost0.0000.0000.0000.0000.0060.9970.997-0.0550.0430.000-0.0211.000

Missing values

2025-10-24T22:32:50.598284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-24T22:32:50.640220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Flight_IDOriginDateFlight_TypeService_TypePassenger_CountProduct_IDProduct_NameStandard_Specification_QtyQuantity_ReturnedQuantity_ConsumedUnit_CostCrew_Feedback
0AM109DOH2025-09-26medium-haulRetail272BRD001Bread Roll Pack627550.35NaN
1AM109DOH2025-09-26medium-haulRetail272CRK075Butter Cookies 75g7414600.75NaN
2AM109DOH2025-09-26medium-haulRetail272DRK023Sparkling Water 330ml12530950.45NaN
3AM109DOH2025-09-26medium-haulRetail272DRK024Still Water 500ml11019910.50NaN
4LX110DOH2025-09-26medium-haulPick & Pack272BRD001Bread Roll Pack177581190.35NaN
5LX110DOH2025-09-26medium-haulPick & Pack272CHO050Chocolate Bar 50g14748990.80NaN
6LX110DOH2025-09-26medium-haulPick & Pack272CRK075Butter Cookies 75g13136950.75drawer incomplete
7LX110DOH2025-09-26medium-haulPick & Pack272DRK023Sparkling Water 330ml205371680.45NaN
8LX110DOH2025-09-26medium-haulPick & Pack272DRK024Still Water 500ml197951020.50NaN
9LX110DOH2025-09-26medium-haulPick & Pack272JCE200Juice 200ml231971340.55NaN
Flight_IDOriginDateFlight_TypeService_TypePassenger_CountProduct_IDProduct_NameStandard_Specification_QtyQuantity_ReturnedQuantity_ConsumedUnit_CostCrew_Feedback
782NH234ZRH2025-10-07short-haulRetail164CRK075Butter Cookies 75g5615410.75NaN
783NH234ZRH2025-10-07short-haulRetail164DRK023Sparkling Water 330ml7420540.45NaN
784NH234ZRH2025-10-07short-haulRetail164JCE200Juice 200ml6516490.55NaN
785NH234ZRH2025-10-07short-haulRetail164SNK001Snack Box Economy437362.10NaN
786QR233ZRH2025-10-07short-haulRetail148CHO050Chocolate Bar 50g416350.80NaN
787QR233ZRH2025-10-07short-haulRetail148CRK075Butter Cookies 75g404360.75NaN
788QR233ZRH2025-10-07short-haulRetail148DRK023Sparkling Water 330ml665610.45NaN
789QR233ZRH2025-10-07short-haulRetail148DRK024Still Water 500ml6413510.50NaN
790QR233ZRH2025-10-07short-haulRetail148HTB110Herbal Tea Bag433400.06ran out early
791QR233ZRH2025-10-07short-haulRetail148JCE200Juice 200ml6112490.55NaN